Towards Automating Code-Reuse Attacks Using Synthesized Gadget Chains


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In the arms race between binary exploitation techniques and mitigation schemes, code-reuse attacks have been proven indispensable. Typically, one of the initial hurdles is that an attacker cannot execute their own code due to countermeasures such as data execution prevention (DEP, ). While this technique is powerful, the task of finding and correctly chaining gadgets remains cumbersome. Although various methods automating this task have been proposed, they either rely on hard-coded heuristics or make specific assumptions about the gadgets’ semantics. This not only drastically limits the search space but also sacrifices their capability to find valid chains unless specific gadgets can be located. As a result, they often produce no chain or an incorrect chain that crashes the program. In this paper, we present SGC, the first generic approach to identify gadget chains in an automated manner without imposing restrictions on the gadgets or limiting its applicability to specific exploitation scenarios. Instead of using heuristics to find a gadget chain, we offload this task to an SMT solver. More specifically, we build a logical formula that encodes the CPU and memory state at the time when the attacker can divert execution flow to the gadget chain, as well as the attacker’s desired program state that the gadget chain should construct. In combination with a logical encoding of the data flow between gadgets, we query an SMT solver whether a valid gadget chain exists. If successful, the solver provides a proof of existence in the form of a synthesized gadget chain. This way, we remain fully flexible w.r.t. to the gadgets. In empirical tests, we find that the solver often uses all types of control-flow transfer instructions and even gadgets with side effects. Our evaluation shows that SGC successfully finds working gadget chains for real-world exploitation scenarios within minutes, even when all state-of-the-art approaches fail.


Software Security
Program Analysis